Topics in Cognitive Science最新文献

This commentary addresses the challenge of linking an individual-grounded theory of concepts to a phenomenon that assumes conceptual conventions at population level (linguistic relativity). We distinguish I-concepts (individual, interior, imagistic) from L-concepts (linguistic, labeled, local) and see that quite different causal processes are often conflated under the term "concepts." I argue that the Grounded Cognition Model (GCM) entails linguistic relativity only to the extent that it imports L-concepts into its scope, which it can hardly avoid doing given that practitioners require language to coordinate around their theory and findings. I conclude that what entails linguistic relativity is not the GCM but language itself.

Cross-linguistic differences in concepts have implications for all theories of concepts, not just for grounded ones. Failure to address these implications does not imply the belief that they do not exist. Instead, it reflects a division of labor between researchers who focus on general principles versus cultural variability. Furthermore, core principles of grounded cognition-empirical learning and situated conceptual processing-predict large cultural differences in conceptual systems. If asked, most grounded cognition researchers would anticipate and endorse these differences, as would most researchers from other perspectives. Finally, by incorporating ethnographic and linguistic analysis, grounded cognition researchers can examine how cultural differences manifest themselves in conceptual systems.

In this commentary, we approach the topic of linguistic relativity from a predictive coding perspective. Discussing the role of "priors" in shaping perception, we argue that language creates an important set of priors for humans, which can affect how sensory information is processed and interpreted. Namely, languages create conventionalized conceptual systems for their speakers, mirroring and reinforcing what is behaviorally important in a society. As such, they create collective conceptual convergence on how to categorize the world and thus "streamline" what people rely on to guide their perception.

Kemmerer's paper convincingly claims that the grounded cognition model (GCM) entails linguistic relativity. Here, we underline that tackling linguistic relativity and cultural differences is vital for GCM. First, it allows GCM to focus more on flexible rather than stable aspects of cognition. Second, it highlights the centrality of linguistic experience for human cognition. While GCM-inspired research underscored the similarity between linguistic and nonlinguistic concepts, it is now paramount to understand when and how language(s) influence knowledge. To this aim, we argue that linguistic variation might be particularly relevant for more abstract concepts-which are more debatable and open to revisions.

In this paper, I respond to eight commentaries on my target article called "Grounded cognition entails linguistic relativity: A neglected implication of a major semantic theory." The commentaries span a broad range of disciplines and perspectives. I have organized my response around the following topics: (1) an introductory synopsis of my main argument; (2) grounded versus amodal theories of concepts; (3) language-specific versus language-independent concepts; (4) language, culture, and cognition; (5) language itself as a source of conceptual grounding; (6) abstract concepts, linguistic relativity, and contextual and individual variability; (7) word meanings as language-specific predictions; and (8) some final remarks about the importance of cross-linguistic diversity.

Kemmerer's discussion of links between semantic typology and embodied cognition is welcome, especially his survey of available evidence. Focusing on mechanisms of embodied enculturation, however, we must understand that language is just one part of developmental assemblies that shape cognition, alongside other cultural elements such as sensory learning, behavior patterns, social interactions, and emotional experience. We believe that a source of this problem is an obsolete definition of "culture" as shared mental information that is inconsistent with models of embodied cognition and yet pervasive in human and cognitive sciences. We point to microethnographies of cognitive ecologies as a tractable remedy.

Kemmerer describes grounded accounts of cognition and, using crosslinguistic diversity across conceptual domains, argues that these accounts entail linguistic relativity. In this comment, I extend Kemmerer's position to the domain of emotion. Emotion concepts exemplify characteristics highlighted by grounded accounts of cognition and differ by culture and language. Recent research further demonstrates considerable situation- and person-specific differences. Based on this evidence, I argue that emotion concepts carry unique implications for variation in meaning and experience, entailing a relativity that is contextual and individual in addition to linguistic. I conclude by considering what such pervasive relativity means for interpersonal understanding.

This paper reviews the recent history of a subset of research in dynamical cognitive science, in particular that subset that allies itself with the sciences of complexity and casts cognitive systems as interaction dominant, noncomputational, and nonmodular. I look at this history in the light of C.S. Peirce's understanding of scientific reasoning as progressing from abduction to deduction to induction. In particular, I examine the development of a controversy concerning the use of the interaction dominance of human cognitive systems as an explanation of the ubiquitous 1/f noise, multifractality, and complexity matching in human behavior.

Fractal fluctuations are a core concept for inquiries into human behavior and cognition from a dynamic systems perspective. Here, we present a generalized variance method for multivariate detrended fluctuation analysis (mvDFA). The advantage of this extension is that it can be applied to multivariate time series and considers intercorrelation between these time series when estimating fractal properties. First, we briefly describe how fractal fluctuations have advanced a dynamic system understanding of cognition. Then, we describe mvDFA in detail and highlight some of the advantages of the approach for simulated data. Furthermore, we show how mvDFA can be used to investigate empirical multivariate data using electroencephalographic recordings during a time-estimation task. We discuss this methodological development within the framework of interaction-dominant dynamics. Moreover, we outline how the availability of multivariate analyses can inform theoretical developments in the area of dynamic systems in human behavior.